Neural networks based on the attractive region for solving global minimization
基于吸引域的总体极小化问题的神经网络求解

Neural networks for solving optimisation problems own very robust real time computation ability and much attention had paid for it in recent years. Neural network method for solving global unconstrained minimized problems was investigated and a new neural network model was then proposed. Starting from method on analysis of attractive region, the global attraction of equilibrium point set was demonstrated for the proposed model. The circuit realization of the given model was analysed and the attraction region for individual equilibrium was estimated also. All these theoretical analyses and estimation results were the foundations for constructing the proposed neural network model. At the same time, they also provide the basic supports for the network's reliable running. In addition, numerical experimental results revealed enough evidence to show that the proposed neural network model could work well in practice on finding solutions for the global minimized problems, which means the model was a very efficient neural network system. The results showed that the proposed neural network model could conduct reliably and stably, whether in theory or in practice, to solve the global minimized problems and hence methods based on the attractive region of neural network construction is a promising research direction for solving optimisation problems.